# -*- coding: utf-8 -*-
from datetime import timedelta
##from utils.operators.spark_sql_operator import SparkSqlOperator


from datetime import timedelta
###from utils.operators.spark_sql_operator import SparkSqlOperator
from utils.operators.cluster_for_spark_sql_operator import SparkSqlOperator
from jms_target_all.time_sensor.time_after_09_30 import time_after_09_30

jms_dwm__dwm_overall_target_network_sum_day_dt = SparkSqlOperator(
    task_id='jms_dwm__dwm_overall_target_network_sum_day_dt',
    task_concurrency=1,
    pool_slots=1,
    master='yarn',
    sla=timedelta(hours=7),
    retries=2,
    execution_timeout=timedelta(hours=3),
    email=['suning@jtexpress.com','yl_bigdata@yl-scm.com'],
    name='jms_dwm__dwm_overall_target_network_sum_day_dt_{{ execution_date | date_add(1) | cst_ds }}',
    sql='jms_target_all/dwm/dwm_overall_target_network_sum_day_dt/execute.hql',
    driver_memory='8G',
    driver_cores=4,
    executor_cores=4,
    executor_memory='10G',
    num_executors=40,
    conf={
        'spark.dynamicAllocation.enabled': 'true',  # 动态资源开启
        'spark.shuffle.service.enabled': 'true',  # 动态资源 Shuffle 服务开启
        'spark.dynamicAllocation.maxExecutors': 80,  # 动态资源最大扩容 Executor 数
        'spark.dynamicAllocation.cachedExecutorIdleTimeout': 120,  # 动态资源自动释放闲置 Executor 的超时时间(s)
        'spark.executor.memoryOverhead': '2G',  # 堆外内存
        'spark.hadoop.hive.exec.dynamic.partition.mode': 'nonstrict', # 动态分区
        'spark.hadoop.hive.exec.dynamic.partition': 'true',
        'spark.yarn.maxAppAttempts':1,
        'spark.sql.shuffle.partitions': 600,
        'spark.driver.maxResultSize': '10G'
    },
    hiveconf={'hive.exec.dynamic.partition': 'true',
              'hive.exec.dynamic.partition.mode': 'nonstrict',
              'hive.exec.max.dynamic.partitions.pernode': 200,
              'hive.exec.max.dynamic.partitions': 200
              },
    yarn_queue='pro',
)

jms_dwm__dwm_overall_target_network_sum_day_dt  << [time_after_09_30]
